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    Secure zones in knowledge graph
    12.
    发明授权
    Secure zones in knowledge graph 有权

    公开(公告)号：US10754963B2

    公开(公告)日：2020-08-25

    申请号：US15904531

    申请日：2018-02-26

    申请人： International Business Machines Corporation

    发明人： Tim U. Scheideler ,  Florian Graf ,  Erik Rueger ,  Marc Albrecht

    IPC分类号： G06F21/60 ,  G06N5/02 ,  G06F21/62

    摘要： Access in a knowledge graph, comprising nodes and edges connecting two or more nodes, is controlled by assigning each node to a non-intersecting zone. A first and second zone identifier, each indicative of a zone occupied by a node where an edge ends, are each stored. Determining an access graph comprising an access node for each zone, access edges representing edges between the zones, and a first list of the zones. Each entry in the first list relates to a zone and a second list of node identifiers, each indicative of a node occupying the zone. A first and second access control list, each related to a zone where an access edge ends and to which a access node in the access graph relates, are stored in each access edge.

    Knowledge graph node expiration
    13.
    发明授权
    Knowledge graph node expiration 有权

    公开(公告)号：US10725982B2

    公开(公告)日：2020-07-28

    申请号：US15817802

    申请日：2017-11-20

    申请人： INTERNATIONAL BUSINESS MACHINES CORPORATION

    发明人： Tim U. Scheideler ,  Thomas Snellgrove ,  Stefan Ravizza ,  Andrea Giovannini ,  Florian Graf ,  Stefan Mayer ,  Erik Rueger

    IPC分类号： G06F16/00 ,  G06F16/215 ,  G06F16/901

    摘要： A method and system for deleting a node of a knowledge graph may be provided. The method comprises receiving knowledge graph data comprising nodes and edges, determining a first relevance degree value for a node and a second relevance degree value for the node, combining the first and the second relevance degree value and marking the node as deletable if the final relevance degree value is above a predefined relevance threshold value. The method comprises further applying queries against the knowledge graph, wherein at least a certain number of such queries invokes accessing of a node, marked as deletable, revising at least one of the first and second relevance degree value based on the accessing of the node marked as deletable, and deleting the node, marked as deletable, if over a predetermined period of time queries against the knowledge graph invoke no access of the node marked as deletable.

    Optimizing user satisfaction when training a cognitive hierarchical storage-management system
    14.
    发明授权
    Optimizing user satisfaction when training a cognitive hierarchical storage-management system 有权

    公开(公告)号：US10705767B2

    公开(公告)日：2020-07-07

    申请号：US15654840

    申请日：2017-07-20

    申请人： International Business Machines Corporation

    发明人： Thorsten Muehge ,  Stefan M. Ravizza ,  Erik Rueger ,  Tim U. Scheideler

    IPC分类号： G06F3/06 ,  G06F16/185 ,  G06N20/00 ,  G06N3/08 ,  G06F16/335

    摘要： A cognitive hierarchical storage-management system receives feedback describing users' satisfaction with the way that one or more prior data-access requests were serviced. The system uses this feedback to associate each previously requested data element's metadata and storage tier with a level of user satisfaction, and to optimize user satisfaction when the system is trained. As feedback continues to be received, the system uses machine-learning methods to identify how closely specific metadata patterns correlate with certain levels of user satisfaction and with certain storage tiers. The system then uses the resulting associations when determining whether to migrate data associated with a particular metadata pattern to a different tier. Data elements may be migrated between different tiers when two metadata sets share metadata values. A user's degree of satisfaction may be encoded as a metadata element that may be used to train a neural network of a machine-learning module. If detecting that two metadata sets share metadata values, the system determines whether to migrate data elements to different tiers.

    Shared scan output in incremental data analysis systems
    17.
    发明授权
    Shared scan output in incremental data analysis systems 有权

    公开(公告)号：US10445294B2

    公开(公告)日：2019-10-15

    申请号：US14636221

    申请日：2015-03-03

    申请人： International Business Machines Corporation

    发明人： Michael Diederich ,  Itzhack Goldberg ,  Erik Rueger ,  Neil Sondhi

    IPC分类号： G06F16/00 ,  G06F16/178 ,  G06F11/14

    摘要： Solutions are provided that use shared scan phases and scan output for various file-level incremental data analysis systems. In one embodiment, a shared scan phase is initiated for a plurality of files in a file system. During the shared scan phase, one or more rules are applied to the files in the file system to identify files on which to perform one or more operations. Shared scan output is created that includes information describing the identified files and operations to be performed on the identified files. Embodiments of the present invention can reduce the amount of time and computing resources that would otherwise be consumed by performing separate walkthroughs of a file system during separate scan phases.

    IDENTIFYING REDUNDANT NODES IN A KNOWLEDGE GRAPH DATA STRUCTURE
    18.
    发明申请
    IDENTIFYING REDUNDANT NODES IN A KNOWLEDGE GRAPH DATA STRUCTURE 审中-公开

    公开(公告)号：US20190235961A1

    公开(公告)日：2019-08-01

    申请号：US15880640

    申请日：2018-01-26

    申请人： INTERNATIONAL BUSINESS MACHINES CORPORATION

    发明人： Andrea Giovannini ,  Tim U. Scheideler ,  Erik Rueger ,  Thomas Snellgrove ,  Stefan Ravizza ,  Florian Graf

    IPC分类号： G06F11/14 ,  G06F17/30

    CPC分类号： G06F11/1448 ,  G06F11/1464 ,  G06F16/951

    摘要： A method, computer system, and computer program product for eliminating a redundant node from a knowledge graph is provided. A structural analysis of a knowledge graph is performed by determining that two nodes have a similar structure. An empirical analysis is performed by determining a search result correlation of potentially redundant nodes, said search result correlation comprising a correlation of search result nodes generated from different search queries to said knowledge graph or a correlation of search results due to selected search result nodes in subtrees of said potentially redundant nodes. Results of said structural analysis and said empirical analysis are combined to generate a redundancy confidence level value for two said nodes. One of said two nodes is determined as redundant. One of said two redundant nodes is removed from the knowledge graph.